Causal inference for all: Marginal causal effects for outcomes truncated by death

Music City Center 

Researchers often express interest in treatment effects that adequately account for post-treatment events (so-called intercurrent events). However, outcome contrasts that naively condition on intercurrent events lack a straightforward causal interpretation, and the practical relevance of other commonly used approaches is debated. In this presentation, I will propose strategies for formulating and choosing an estimand, beyond the marginal intention-to-treat effect, from the perspective of a decision maker and treatment developer. I will emphasize that a well-articulated, practically useful research question should either reflect decision-making at this point in time or future treatment development. A common feature of estimands that are practically useful is their correspondence to possibly hypothetical but well-defined interventions in identifiable (sub)populations. To illustrate my points, I will consider examples that have recently motivated the consideration of principal stratum estimands in clinical trials. In all of these examples, I will suggest alternative causal estimands that align with explicit research questions of practical interest and require less stringent identification assumptions.

Causal inference

Estimands

Intercurrent events

Survival analysis

Biostatistics

Randomized experiments